TY - JOUR
T1 - Predicting renal cancer recurrence
T2 - Defining limitations of existing prognostic models with prospective trial-based validation
AU - Correa, Andres F.
AU - Jegede, Opeyemi
AU - Haas, Naomi B.
AU - Flaherty, Keith T.
AU - Pins, Michael R.
AU - Messing, Edward M.
AU - Manola, Judith
AU - Wood, Christopher G.
AU - Kane, Christopher J.
AU - Jewett, Michael A.S.
AU - Dutcher, Janice P.
AU - DiPaola, Robert S.
AU - Carducci, Michael A.
AU - Uzzo, Robert G.
N1 - Funding Information:
Supported by Grants No. CA180820, CA180794, CA180867, CA180858, CA180888, CA180821, CA180863 from the National Cancer Institute, National Institutes of Health, and Grant No. 704970 from the Canadian Cancer Society.
Publisher Copyright:
© 2019 by American Society of Clinical Oncology
PY - 2019/8/10
Y1 - 2019/8/10
N2 - PURPOSE To validate currently used recurrence prediction models for renal cell carcinoma (RCC) by using prospective data from the ASSURE (ECOG-ACRIN E2805; Adjuvant Sorafenib or Sunitinib for Unfavorable Renal Carcinoma) adjuvant trial. PATIENTS AND METHODS Eight RCC recurrence models (University of California at Los Angeles Integrated Staging System [UISS]; Stage, Size, Grade, and Necrosis [SSIGN]; Leibovich; Kattan; Memorial Sloan Kettering Cancer Center [MSKCC]; Yaycioglu; Karakiewicz; and Cindolo) were selected on the basis of their use in clinical practice and clinical trial designs. These models along with the TNM staging system were validated using 1,647 patients with resected localized high-grade or locally advanced disease ($ pT1b grade 3 and 4/pTanyN1Mo) from the ASSURE cohort. The predictive performance of the model was quantified by assessing its discriminatory and calibration abilities. RESULTS Prospective validation of predictive and prognostic models for localized RCC showed a substantial decrease in each of the predictive abilities of the model compared with their original and externally validated discriminatory estimates. Among the models, the SSIGN score performed best (0.688; 95% CI, 0.686 to 0.689), and the UISS model performed worst (0.556; 95% CI, 0.555 to 0.557). Compared with the 2002 TNM staging system (C-index, 0.60), most models only marginally outperformed standard staging. Importantly, all models, including TNM, demonstrated statistically significant variability in their predictive ability over time and were most useful within the first 2 years after diagnosis. CONCLUSION In RCC, as in many other solid malignancies, clinicians rely on retrospective prediction tools to guide patient care and clinical trial selection and largely overestimate their predictive abilities. We used prospective collected adjuvant trial data to validate existing RCC prediction models and demonstrate a sharp decrease in the predictive ability of all models compared with their previous retrospective validations. Accordingly, we recommend prospective validation of any predictive model before implementing it into clinical practice and clinical trial design.
AB - PURPOSE To validate currently used recurrence prediction models for renal cell carcinoma (RCC) by using prospective data from the ASSURE (ECOG-ACRIN E2805; Adjuvant Sorafenib or Sunitinib for Unfavorable Renal Carcinoma) adjuvant trial. PATIENTS AND METHODS Eight RCC recurrence models (University of California at Los Angeles Integrated Staging System [UISS]; Stage, Size, Grade, and Necrosis [SSIGN]; Leibovich; Kattan; Memorial Sloan Kettering Cancer Center [MSKCC]; Yaycioglu; Karakiewicz; and Cindolo) were selected on the basis of their use in clinical practice and clinical trial designs. These models along with the TNM staging system were validated using 1,647 patients with resected localized high-grade or locally advanced disease ($ pT1b grade 3 and 4/pTanyN1Mo) from the ASSURE cohort. The predictive performance of the model was quantified by assessing its discriminatory and calibration abilities. RESULTS Prospective validation of predictive and prognostic models for localized RCC showed a substantial decrease in each of the predictive abilities of the model compared with their original and externally validated discriminatory estimates. Among the models, the SSIGN score performed best (0.688; 95% CI, 0.686 to 0.689), and the UISS model performed worst (0.556; 95% CI, 0.555 to 0.557). Compared with the 2002 TNM staging system (C-index, 0.60), most models only marginally outperformed standard staging. Importantly, all models, including TNM, demonstrated statistically significant variability in their predictive ability over time and were most useful within the first 2 years after diagnosis. CONCLUSION In RCC, as in many other solid malignancies, clinicians rely on retrospective prediction tools to guide patient care and clinical trial selection and largely overestimate their predictive abilities. We used prospective collected adjuvant trial data to validate existing RCC prediction models and demonstrate a sharp decrease in the predictive ability of all models compared with their previous retrospective validations. Accordingly, we recommend prospective validation of any predictive model before implementing it into clinical practice and clinical trial design.
UR - http://www.scopus.com/inward/record.url?scp=85070778796&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85070778796&partnerID=8YFLogxK
U2 - 10.1200/JCO.19.00107
DO - 10.1200/JCO.19.00107
M3 - Article
C2 - 31216227
AN - SCOPUS:85070778796
SN - 0732-183X
VL - 37
SP - 2062
EP - 2071
JO - Journal of Clinical Oncology
JF - Journal of Clinical Oncology
IS - 23
ER -